Article

Comparison of Accelerometer Cut Points for Predicting Activity Intensity in Youth

Department of Nutrition and Exercise Sciences, Oregon State University, Corvallis, OR 97331, USA.
Medicine and science in sports and exercise (Impact Factor: 3.98). 12/2010; 43(7):1360-8. DOI: 10.1249/MSS.0b013e318206476e
Source: PubMed

ABSTRACT

The absence of comparative validity studies has prevented researchers from reaching consensus regarding the application of intensity-related accelerometer cut points for children and adolescents.
This study aimed to evaluate the classification accuracy of five sets of independently developed ActiGraph cut points using energy expenditure, measured by indirect calorimetry, as a criterion reference standard.
A total of 206 participants between the ages of 5 and 15 yr completed 12 standardized activity trials. Trials consisted of sedentary activities (lying down, writing, computer game), lifestyle activities (sweeping, laundry, throw and catch, aerobics, basketball), and ambulatory activities (comfortable walk, brisk walk, brisk treadmill walk, running). During each trial, participants wore an ActiGraph GT1M, and V˙O2 was measured breath-by-breath using the Oxycon Mobile portable metabolic system. Physical activity intensity was estimated using five independently developed cut points: Freedson/Trost (FT), Puyau (PU), Treuth (TR), Mattocks (MT), and Evenson (EV). Classification accuracy was evaluated via weighted κ statistics and area under the receiver operating characteristic curve (ROC-AUC).
Across all four intensity levels, the EV (κ=0.68) and FT (κ=0.66) cut points exhibited significantly better agreement than TR (κ=0.62), MT (κ=0.54), and PU (κ=0.36). The EV and FT cut points exhibited significantly better classification accuracy for moderate- to vigorous-intensity physical activity (ROC-AUC=0.90) than TR, PU, or MT cut points (ROC-AUC=0.77-0.85). Only the EV cut points provided acceptable classification accuracy for all four levels of physical activity intensity and performed well among children of all ages. The widely applied sedentary cut point of 100 counts per minute exhibited excellent classification accuracy (ROC-AUC=0.90).
On the basis of these findings, we recommend that researchers use the EV ActiGraph cut points to estimate time spent in sedentary, light-, moderate-, and vigorous-intensity activity in children and adolescents.

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Available from: Karin Pfeiffer, Feb 14, 2015
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    • "500 min of data between 05:00 and 23:59). Mean minutes of daily MVPA will be estimated using the Evenson[33]cut-point which has been found to be the most accurate threshold for adolescents[34]. The intervention could reduce the amount of time participants spend sedentary and/or their mode of travel to school (i.e. "

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    • "Accelerometer data were treated in Actilife v.6 (Acti- Graph LLC, FL, USA). Time sedentary and in MVPA were determined using age-appropriate accelerometer count cut points[38,39]. Bouts of 20 min or more of continuous zero counts were excluded from the data and considered as non-wear time[40]. "
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    ABSTRACT: Adaptation of physical activity self-report questionnaires is sometimes required to reflect the activity behaviours of diverse populations. The processes used to modify self-report questionnaires though are typically underreported. This two-phased study used a formative approach to investigate the validity and reliability of the Physical Activity Questionnaire for Adolescents (PAQ-A) in English youth. Phase one examined test content and response process validity and subsequently informed a modified version of the PAQ-A. Phase two assessed the validity and reliability of the modified PAQ-A. In phase one, focus groups (n = 5) were conducted with adolescents (n = 20) to investigate test content and response processes of the original PAQ-A. Based on evidence gathered in phase one, a modified version of the questionnaire was administered to participants (n = 169, 14.5 ± 1.7 years) in phase two. Internal consistency and test-retest reliability were assessed using Cronbach’s alpha and intra-class correlations, respectively. Spearman correlations were used to assess associations between modified PAQ-A scores and accelerometer-derived physical activity, self-reported fitness and physical activity self-efficacy. Phase one revealed that the original PAQ-A was unrepresentative for English youth and that item comprehension varied. Contextual and population/cultural-specific modifications were made to the PAQ-A for use in the subsequent phase. In phase two, modified PAQ-A scores had acceptable internal consistency (α = 0.72) and test-retest reliability (ICC = 0.78). Modified PAQ-A scores were significantly associated with objectively assessed moderate-to-vigorous physical activity (r = 0.39), total physical activity (r = 0.42), self-reported fitness (r = 0.35), and physical activity self-efficacy (r = 0.32) (p ≤ 0.01). The modified PAQ-A had acceptable internal consistency and test-retest reliability. Modified PAQ-A scores displayed weak-to-moderate correlations with objectively measured physical activity, self-reported fitness, and self-efficacy providing evidence of satisfactory criterion and construct validity, respectively. Further testing with more diverse English samples is recommended to provide a more complete assessment of the tool.
    Full-text · Article · Dec 2016
    • "Most accelerometer-based PA monitors output arbitrary representations of acceleration known as counts. This unit is not physiologically meaningful, but has been widely used to predict energy expenditure (kilocalories, METs) through statistical modeling (e.g., linear regression equations) in different age groups (Freedson, Melanson, & Sirard, 1998; Strath et al., 2012; Trost, Loprinzi, Moore, & Pfeiffer, 2011). Human movement can be classified based on anatomical planes that correspond to triaxial detection of acceleration using accelerometers. "

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